The future of AI careers (after the creative AI revolution)
Sebastian Thrun he said: “No one says so, but I think artificial intelligence is almost a humanities discipline. It’s really an attempt to understand human intelligence and human knowledge.”
The way many people work could be fundamentally changed by creative AI. Some people may be excited by this concept. What this means for others can be disturbing. In industries where automation is possible, there is no doubt that this technology has the potential to greatly increase productivity and save costs. This may result in job losses or at least fewer new jobs in selected areas. On the other hand, it should be noted that the use of generative AI can lead to lower prices, which can increase access to related goods and services for customers from all walks of life and different industries, such as education and health. , can increase production volumes and ultimately create more job opportunities. The way we work will continue to evolve as a result of our creativity and imagination, many of today’s jobs will flourish and new ones will develop.
To understand how creative AI will disrupt the job market and how it has impacted so far and what it may have in the future, we held a panel discussion with industry leaders to provide a fresher perspective. The session was moderated Anees MerchantEVP – Global Growth and Client Success at Course5i along with panelists Karthik RameshVP – Customer Partner – US Supplier & Life Sciences at Emids, Rahul ThotaFounder and CEO of Akaike Technologies, Rathnakumar UdayakumarHead of Product at Netradyne, Deepika KaushalVice President of Piramal Capital & Housing Finance and Lavi NigamPrincipal Data Scientist, Google Cloud AI Ecosystem.
Are we ready?
Technology is growing. It’s making progress, but the use cases you implement it in and under what circumstances are very different. Few companies are advanced but, for most, they are in a basic state. Some are in the early stages of consumer AI. How do we actually consume the basic stuff, put the basic stuff in and then get into the good analytical stuff that will drive results. Technology is moving fast and very few companies can keep up with the pace as well as the people who are willing to adopt it.
—Deepika Kaushal, Vice President, Piramal Capital & Housing Finance
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Multimodal
The store depends on who we sell to. Educating customers is something we will have to do continuously. But ultimately, it depends on who we are talking to and how we are packaged. Closing a new client takes less effort than closing a senior data scientist. It is not easy to get the right talent who have experience working with different data modalities, especially with the latest technologies. For a good talent, we have to look at them a lot and it’s not a great experience. For our business, it’s about getting the right talent that limits the rate limit. Alternatively, in terms of market readiness for such solutions, customers are ready to adopt the solutions.
—Rahul Thota, Founder and CEO, Akaike Technologies
An approach to the skills of employers
The perfect analogy to make is ten years ago, it was a parallel triangle. There are people that companies are looking for and you can find that talent at every level. Now, there are different places where you can go and order them. Certainly, in the last couple of years, the landscape has changed in a way that many of us did not expect, for technology to jump so quickly. All of a sudden, what has happened is that the skill set has become niche and supply and demand are now out of the equation. What is really happening is that you will find talented people, but for those with the right skill set, the abundance of opportunity is even greater. So connecting someone to a startup ecosystem is getting harder and harder. At this point, any talent we think has the right skill set is far and few between and they have enough opportunity on their doorstep.
—Rathnakumar Udayakumar, Product Manager, Netradyne
Can organizations keep up with skills?
This problem has been magnified in terms of the choice and availability of talent. If you asked me to address this problem today, you have the rapid growth of technology that threatens to surpass human talent in scale and speed. Initially we focus on intra-company investments. In terms of innovation, we work not only on next-horizon technologies, but also with academia. It’s not just about going to campuses and doing events, but very proactively working with them to take on some of the latest problems in AI/ML. This will always be a niche area as the market and potential continue to grow, we will need to look at innovative ways to supply it.
—Karthik Ramesh, VP – Customer Partner – US Supplier & Life Sciences at Emids
Scale: A skill challenge
As for scaling, it has two aspects. One is scaling people and the other is the value of scaling. It could come from scaling some tools or some IPs. In the second aspect, there is a lot of work with the many no-code tools available. Individual productivity itself is scaling. For example: the job of automating invitation engineering is being done by providing data and then using algorithms that find the best invitations that deliver the required outputs. It may not be true today, but going forward, it may be that having people who are proficient in English is all we want for that.
—Rahul Thota, Founder and CEO, Akaike Technologies
When you look at scaling, you’re looking at both vertical and horizontal scale. At this point, the way is to scale to technology rather than human resources. This is precisely what technology is scaling up. There are ways to do it much faster than most people can, with the help of a co-pilot. Even if the co-pilot is just an assistant, he can work with someone who can do things on his own to speed things up even more without the need for a large team.
—Rathnakumar Udayakumar, Product Manager, Netradyne
Employee Perspective
One of the biggest myths of the data science or AI journey is the engineering culture. What we don’t do, and I think it’s important, we don’t embed engineering culture and data science. To be a better data scientist or machine learning engineer, we have to internalize those basic fundamental engineering practices, when you go into an industry and you work in these models, in the basic models, deep learning ultimately has to be part of the stack. , already driven by software engineering. It is important to adapt to the evolution of what we are teaching students, to teach less in a stack of notebooks and more in the engineering sense.
—Lavi Nigam, Principal Data Scientist, Google Cloud AI Ecosystem
How to build a career today?
You have to constantly build innovative ways of learning and learning doesn’t have to be from various online institutes, it can also be in-house learning. You need to constantly build your skill set. Today, you have so many ways to do it online, forget the formal education and higher institutes that give certificate alumni status, but many ways to learn things yourself. Your AI talent problems won’t be solved by hiring more AI data scientists. Some of these technology trends, we talked about generative AI, robust engineering, cryptocurrency, blockchain, they’re all there. But they won’t replace jobs or skill sets. You look for a person who comes with the desire and desire to learn and unlearn. It’s not just about data science, statistics and machine learning, it’s fundamentally about being adaptive.
—Karthik Ramesh, VP – Customer Partner – US Supplier & Life Sciences at Emids
Shu Ha Ri – stages of learning on the way to mastery
From the perspective of the education industry, I think we’re only preparing for a job because we’re doing it in an MBA-like way, we’re not going anywhere. Ten years ago, when we were building predictive models, it was about hypothesis testing, null hypothesis making those models predictive models. Now ML is a new word. So everyone started learning and now we see it’s a black box. But from the perspective of formal education, it should indicate that learning things creates interest. The education system should focus more on building its skills than solving problems. Continue to learn in the technology environment because technology is evolving as we speak, and incorporate a business component.
—Deepika Kaushal, Vice President, Piramal Capital & Housing Finance
Since 2017, and especially in the last decade, the inventive human mind has improved the status of creative AI. Human intelligence created new deep learning architectures, statistical text analysis techniques, and training methodologies for AI models using the vast online “literature”. The Lovelace effect indicated that artificial intelligence cannot incorporate human creativity and imagination.
